thesis statement on gender wage gap

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The persistence of pay inequality: The gender pay gap in an anonymous online labor market

Leib litman.

1 Department of Psychology, Lander College, Flushing, New York, United States of America

Jonathan Robinson

2 Department of Computer Science, Lander College, Flushing, New York, United States of America

3 Department of Health Policy & Management, Mailman School of Public Health, Columbia University, New York, New York, United States of America

Cheskie Rosenzweig

4 Department of Clinical Psychology, Columbia University, New York, New York, United States of America

Joshua Waxman

5 Department of Computer Science, Stern College for Women, New York, New York, United States of America

Lisa M. Bates

6 Department of Epidemiology, Mailman School of Public Health, Columbia University New York, New York, United States of America

Associated Data

Due to the sensitive nature of some of the data, and the terms of service of the websites used during data collection (including CloudResearch and MTurk), CloudResearch cannot release the full data set to make it publically available. The data are on CloudResearch's Sequel servers located at Queens College in the city of New York. CloudResearch makes data available to be accessed by researchers for replication purposes, on the CloudResearch premises, in the same way the data were accessed and analysed by the authors of this manuscript. The contact person at CloudResearch who can help researchers access the data set is Tzvi Abberbock, who can be reached at [email protected] .

Studies of the gender pay gap are seldom able to simultaneously account for the range of alternative putative mechanisms underlying it. Using CloudResearch, an online microtask platform connecting employers to workers who perform research-related tasks, we examine whether gender pay discrepancies are still evident in a labor market characterized by anonymity, relatively homogeneous work, and flexibility. For 22,271 Mechanical Turk workers who participated in nearly 5 million tasks, we analyze hourly earnings by gender, controlling for key covariates which have been shown previously to lead to differential pay for men and women. On average, women’s hourly earnings were 10.5% lower than men’s. Several factors contributed to the gender pay gap, including the tendency for women to select tasks that have a lower advertised hourly pay. This study provides evidence that gender pay gaps can arise despite the absence of overt discrimination, labor segregation, and inflexible work arrangements, even after experience, education, and other human capital factors are controlled for. Findings highlight the need to examine other possible causes of the gender pay gap. Potential strategies for reducing the pay gap on online labor markets are also discussed.

Introduction

The gender pay gap, the disparity in earnings between male and female workers, has been the focus of empirical research in the US for decades, as well as legislative and executive action under the Obama administration [ 1 , 2 ]. Trends dating back to the 1960s show a long period in which women’s earnings were approximately 60% of their male counterparts, followed by increases in women’s earnings starting in the 1980s, which began to narrow, but not close, the gap which persists today [ 3 ]. More recent data from 2014 show that overall, the median weekly earnings of women working full time were 79–83% of what men earned [ 4 – 9 ].

The extensive literature seeking to explain the gender pay gap and its trajectory over time in traditional labor markets suggests it is a function of multiple structural and individual-level processes that reflect both the near-term and cumulative effects of gender relations and roles over the life course. Broadly speaking, the drivers of the gender pay gap can be categorized as: 1) human capital or productivity factors such as education, skills, and workforce experience; 2) industry or occupational segregation, which some estimates suggest accounts for approximately half of the pay gap; 3) gender-specific temporal flexibility constraints which can affect promotions and remuneration; and finally, 4) gender discrimination operating in hiring, promotion, task assignment, and/or compensation. The latter mechanism is often estimated by inference as a function of unexplained residual effects of gender on payment after accounting for other factors, an approach which is most persuasive in studies of narrowly restricted populations of workers such as lawyers [ 10 ] and academics of specific disciplines [ 11 ]. A recent estimate suggests this unexplained gender difference in earnings can account for approximately 40% of the pay gap [ 3 ]. However, more direct estimations of discriminatory processes are also available from experimental evidence, including field audit and lab-based studies [ 12 – 14 ]. Finally, gender pay gaps have also been attributed to differential discrimination encountered by men and women on the basis of parental status, often known as the ‘motherhood penalty’ [ 15 ].

Non-traditional ‘gig economy’ labor markets and the gender pay gap

In recent years there has been a dramatic rise in nontraditional ‘gig economy’ labor markets, which entail independent workers hired for single projects or tasks often on a short-term basis with minimal contractual engagement. “Microtask” platforms such as Amazon Mechanical Turk (MTurk) and Crowdflower have become a major sector of the gig economy, offering a source of easily accessible supplementary income through performance of small tasks online at a time and place convenient to the worker. Available tasks can range from categorizing receipts to transcription and proofreading services, and are posted online by the prospective employer. Workers registered with the platform then elect to perform the advertised tasks and receive compensation upon completion of satisfactory work [ 16 ]. An estimated 0.4% of US adults are currently receiving income from such platforms each month [ 17 ], and microtask work is a growing sector of the service economy in the United States [ 18 ]. Although still relatively small, these emerging labor market environments provide a unique opportunity to investigate the gender pay gap in ways not possible within traditional labor markets, due to features (described below) that allow researchers to simultaneously account for multiple putative mechanisms thought to underlie the pay gap.

The present study utilizes the Amazon Mechanical Turk (MTurk) platform as a case study to examine whether a gender pay gap remains evident when the main causes of the pay gap identified in the literature do not apply or can be accounted for in a single investigation. MTurk is an online microtask platform that connects employers (‘requesters’) to employees (‘workers’) who perform jobs called “Human Intelligence Tasks” (HITs). The platform allows requesters to post tasks on a dashboard with a short description of the HIT, the compensation being offered, and the time the HIT is expected to take. When complete, the requester either approves or rejects the work based on quality. If approved, payment is quickly accessible to workers. The gender of workers who complete these HITs is not known to the requesters, but was accessible to researchers for the present study (along with other sociodemographic information and pay rates) based on metadata collected through CloudResearch (formerly TurkPrime), a platform commonly used to conduct social and behavioral research on MTurk [ 19 ].

Evaluating pay rates of workers on MTurk requires estimating the pay per hour of each task that a worker accepts which can then be averaged together. All HITs posted on MTurk through CloudResearch display how much a HIT pays and an estimated time that it takes for that HIT to be completed. Workers use this information to determine what the corresponding hourly pay rate of a task is likely to be, and much of our analysis of the gender pay gap is based on this advertised pay rate of all completed surveys. We also calculate an estimate of the gender pay gap based on actual completion times to examine potential differences in task completion speed, which we refer to as estimated actual wages (see Methods section for details).

Previous studies have found that both task completion time and the selection of tasks influences the gender pay gap in at least some gig economy markets. For example, a gender pay gap was observed among Uber drivers, with men consistently earning higher pay than women [ 20 ]. Some of the contributing factors to this pay gap include that male Uber drivers selected different tasks than female drivers, including being more willing to work at night and to work in neighborhoods that were perceived to be more dangerous. Male drivers were also likely to drive faster than their female counterparts. These findings show that person-level factors like task selection, and speed can influence the gender pay gap within gig economy markets.

MTurk is uniquely suited to examine the gender pay gap because it is possible to account simultaneously for multiple structural and individual-level factors that have been shown to produce pay gaps. These include discrimination, work heterogeneity (leading to occupational segregation), and job flexibility, as well as human capital factors such as experience and education.

Discrimination

When employers post their HITs on MTurk they have no way of knowing the demographic characteristics of the workers who accept those tasks, including their gender. While MTurk allows for selective recruitment of specific demographic groups, the MTurk tasks examined in this study are exclusively open to all workers, independent of their gender or other demographic characteristics. Therefore, features of the worker’s identity that might be the basis for discrimination cannot factor into an employer’s decision-making regarding hiring or pay.

Task heterogeneity

Another factor making MTurk uniquely suited for the examination of the gender pay gap is the relative homogeneity of tasks performed by the workers, minimizing the potential influence of gender differences in the type of work pursued on earnings and the pay gap. Work on the MTurk platform consists mostly of short tasks such as 10–15 minute surveys and categorization tasks. In addition, the only information that workers have available to them to choose tasks, other than pay, is the tasks’ titles and descriptions. We additionally classified tasks based on similarity and accounted for possible task heterogeneity effects in our analyses.

Job flexibility

MTurk is not characterized by the same inflexibilities as are often encountered in traditional labor markets. Workers can work at any time of the day or day of the week. This increased flexibility may be expected to provide more opportunities for participation in this labor market for those who are otherwise constrained by family or other obligations.

Human capital factors

It is possible that the more experienced workers could learn over time how to identify higher paying tasks by virtue of, for example, identifying qualities of tasks that can be completed more quickly than the advertised required time estimate. Further, if experience is correlated with gender, it could contribute to a gender pay gap and thus needs to be controlled for. Using CloudResearch metadata, we are able to account for experience on the platform. Additionally, we account for multiple sociodemographic variables, including age, marital status, parental status, education, income (from all sources), and race using the sociodemographic data available through CloudResearch.

Expected gender pay gap findings on MTurk

Due to the aforementioned factors that are unique to the MTurk marketplace–e.g., anonymity, self-selection into tasks, relative homogeneity of the tasks performed, and flexible work scheduling–we did not expect a gender pay gap to be evident on the platform to the same extent as in traditional labor markets. However, potential gender differences in task selection and completion speed, which have implications for earnings, merit further consideration. For example, though we expect the relative homogeneity of the MTurk tasks to minimize gender differences in task selection that could mimic occupational segregation, we do account for potential subtle residual differences in tasks that could differentially attract male and female workers and indirectly lead to pay differentials if those tasks that are preferentially selected by men pay a higher rate. To do this we categorize all tasks based on their descriptions using K-clustering and add the clusters as covariates to our models. In addition, we separately examine the gender pay gap within each topic-cluster.

In addition, if workers who are experienced on the platform are better able to find higher paying HITs, and if experience is correlated with gender, it may lead to gender differences in earnings. Theoretically, other factors that may vary with gender could also influence task selection. Previous studies of the pay gap in traditional markets indicate that reservation wages, defined as the pay threshold at which a person is willing to accept work, may be lower among women with children compared to women without, and to that of men as well [ 21 ]. Thus, if women on MTurk are more likely to have young children than men, they may be more willing to accept available work even if it pays relatively poorly. Other factors such as income, education level, and age may similarly influence reservation wages if they are associated with opportunities to find work outside of microtask platforms. To the extent that these demographics correlate with gender they may give rise to a gender pay gap. Therefore we consider age, experience on MTurk, education, income, marital status, and parental status as covariates in our models.

Task completion speed may vary by gender for several reasons, including potential gender differences in past experience on the platform. We examine the estimated actual pay gap per hour based on HIT payment and estimated actual completion time to examine the effects of completion speed on the wage gap. We also examine the gender pay gap based on advertised pay rates, which are not dependent on completion speed and more directly measure how gender differences in task selection can lead to a pay gap. Below, we explain how these were calculated based on meta-data from CloudResearch.

To summarize, the overall goal of the present study was to explore whether gender pay differentials arise within a unique, non-traditional and anonymous online labor market, where known drivers of the gender pay gap either do not apply or can be accounted for statistically.

Materials and methods

Amazon mechanical turk and cloudresearch.

Started in 2005, the original purpose of the Amazon Mechanical Turk (MTurk) platform was to allow requesters to crowdsource tasks that could not easily be handled by existing technological solutions such as receipt copying, image categorization, and website testing. As of 2010, researchers increasingly began using MTurk for a wide variety of research tasks in the social, behavioral, and medical sciences, and it is currently used by thousands of academic researchers across hundreds of academic departments [ 22 ]. These research-related HITs are typically listed on the platform in generic terms such as, “Ten-minute social science study,” or “A study about public opinion attitudes.”

Because MTurk was not originally designed solely for research purposes, its interface is not optimized for some scientific applications. For this reason, third party add-on toolkits have been created that offer critical research tools for scientific use. One such platform, CloudResearch (formerly TurkPrime), allows requesters to manage multiple research functions, such as applying sampling criteria and facilitating longitudinal studies, through a link to their MTurk account. CloudResearch’s functionality has been described extensively elsewhere [ 19 ]. While the demographic characteristics of workers are not available to MTurk requesters, we were able to retroactively identify the gender and other demographic characteristics of workers through the CloudResearch platform. CloudResearch also facilitates access to data for each HIT, including pay, estimated length, and title.

The study was an analysis of previously collected metadata, which were analyzed anonymously. We complied with the terms of service for all data collected from CloudResearch, and MTurk. The approving institutional review board for this study was IntegReview.

Analytic sample

We analyzed the nearly 5 million tasks completed during an 18-month period between January 2016 and June 2017 by 12,312 female and 9,959 male workers who had complete data on key demographic characteristics. To be included in the analysis a HIT had to be fully completed, not just accepted, by the worker, and had to be accepted (paid for) by the requester. Although the vast majority of HITs were open to both males and females, a small percentage of HITs are intended for a specific gender. Because our goal was to exclusively analyze HITs for which the requesters did not know the gender of workers, we excluded any HITs using gender-specific inclusion or exclusion criteria from the analyses. In addition, we removed from the analysis any HITs that were part of follow-up studies in which it would be possible for the requester to know the gender of the worker from the prior data collection. Finally, where possible, CloudResearch tracks demographic information on workers across multiple HITs over time. To minimize misclassification of gender, we excluded the 0.3% of assignments for which gender was unknown with at least 95% consistency across HITs.

The main exposure variable is worker gender and the outcome variables are estimated actual hourly pay accrued through completing HITs, and advertised hourly pay for completed HITs. Estimated actual hourly wages are based on the estimated length in minutes and compensation in dollars per HIT as posted on the dashboard by the requester. We refer to actual pay as estimated because sometimes people work multiple assignments at the same time (which is allowed on the platform), or may simultaneously perform other unrelated activities and therefore not work on the HIT the entire time the task is open. We also considered several covariates to approximate human capital factors that could potentially influence earnings on this platform, including marital status, education, household income, number of children, race/ethnicity, age, and experience (number of HITs previously completed). Additional covariates included task length, task cluster (see below), and the serial order with which workers accepted the HIT in order to account for potential differences in HIT acceptance speed that may relate to the pay gap.

Database and analytic approach

Data were exported from CloudResearch’s database into Stata in long-form format to represent each task on a single row. For the purposes of this paper, we use “HIT” and “study” interchangeably to refer to a study put up on the MTurk dashboard which aims to collect data from multiple participants. A HIT or study consist of multiple “assignments” which is a single task completed by a single participant. Columns represented variables such as demographic information, payment, and estimated HIT length. Column variables also included unique IDs for workers, HITs (a single study posted by a requester), and requesters, allowing for a multi-level modeling analytic approach with assignments nested within workers. Individual assignments (a single task completed by a single worker) were the unit of analysis for all models.

Linear regression models were used to calculate the gender pay gap using two dependent variables 1) women’s estimated actual earnings relative to men’s and 2) women’s selection of tasks based on advertised earnings relative to men’s. We first examined the actual pay model, to see the gender pay gap when including an estimate of task completion speed, and then adjusted this model for advertised hourly pay to determine if and to what extent a propensity for men to select more remunerative tasks was evident and driving any observed gender pay gap. We additionally ran separate models using women’s advertised earnings relative to men’s as the dependent variable to examine task selection effects more directly. The fully adjusted models controlled for the human capital-related covariates, excluding household income and education which were balanced across genders. These models also tested for interactions between gender and each of the covariates by adding individual interaction terms to the adjusted model. To control for within-worker clustering, Huber-White standard error corrections were used in all models.

Cluster analysis

To explore the potential influence of any residual task heterogeneity and gender preference for specific task type as the cause of the gender pay gap, we use K-means clustering analysis (seed = 0) to categorize the types of tasks into clusters based on the descriptions that workers use to choose the tasks they perform. We excluded from this clustering any tasks which contained certain gendered words (such as “male”, “female”, etc.) and any tasks which had fewer than 30 respondents. We stripped out all punctuation, symbols and digits from the titles, so as to remove any reference to estimated compensation or duration. The features we clustered on were the presence or absence of 5,140 distinct words that appeared across all titles. We then present the distribution of tasks across these clusters as well as average pay by gender and the gender pay gap within each cluster.

The demographics of the analytic sample are presented in Table 1 . Men and women completed comparable numbers of tasks during the study period; 2,396,978 (48.6%) for men and 2,539,229 (51.4%) for women.

In Table 2 we measure the differences in remuneration between genders, and then decompose any observed pay gap into task completion speed, task selection, and then demographic and structural factors. Model 1 shows the unadjusted regression model of gender differences in estimated actual pay, and indicates that, on average, tasks completed by women paid 60 (10.5%) cents less per hour compared to tasks completed by men (t = 17.4, p < .0001), with the mean estimated actual pay across genders being $5.70 per hour.

*Model adjusted for race, marital status, number of children and task clusters as categorical covariates, and age, HIT acceptance speed, and number of HITs as continuous covariates.

In Model 2, adjusting for advertised hourly pay, the gender pay gap dropped to 46 cents indicating that 14 cents of the pay gap is attributable to gender differences in the selection of tasks (t = 8.6, p < .0001). Finally, after the inclusion of covariates and their interactions in Model 3, the gender pay differential was further attenuated to 32 cents (t = 6.7, p < .0001). The remaining 32 cent difference (56.6%) in earnings is inferred to be attributable to gender differences in HIT completion speed.

Task selection analyses

Although completion speed appears to account for a significant portion of the pay gap, of particular interest are gender differences in task selection. Beyond structural factors such as education, household composition and completion speed, task selection accounts for a meaningful portion of the gender pay gap. As a reminder, the pay rate and expected completion time are posted for every HIT, so why women would select less remunerative tasks on average than men do is an important question to explore. In the next section of the paper we perform a set of analyses to examine factors that could account for this observed gender difference in task selection.

Advertised hourly pay

To examine gender differences in task selection, we used linear regression to directly examine whether the advertised hourly pay differed for tasks accepted by male and female workers. We first ran a simple model ( Table 3 ; Model 3A) on the full dataset of 4.93 million HITs, with gender as the predictor and advertised hourly pay as the outcome including no other covariates. The unadjusted regression results (Model 4) shown in Table 3 , indicates that, summed across all clusters and demographic groups, tasks completed by women were advertised as paying 28 cents (95% CI: $0.25-$0.31) less per hour (5.8%) compared to tasks completed by men (t = 21.8, p < .0001).

*Models adjusted for race, marital status, number of children, and task clusters as categorical covariates, and age, HIT acceptance speed, and number of HITs as continuous covariates.

Model 5 examines whether the remuneration differences for tasks selected by men and women remains significant in the presence of multiple covariates included in the previous model and their interactions. The advertised pay differential for tasks selected by women compared to men was attenuated to 21 cents (4.3%), and remained statistically significant (t = 9.9, p < .0001). This estimate closely corresponded to the inferred influence of task selection reported in Table 2 . Tests of gender by covariate interactions were significant only in the cases of age and marital status; the pay differential in tasks selected by men and women decreased with age and was more pronounced among single versus currently or previously married women.

To further examine what factors may account for the observed gender differences in task selection we plotted the observed pay gap within demographic and other covariate groups. Table 4 shows the distribution of tasks completed by men and women, as well as mean earnings and the pay gap across all demographic groups, based on the advertised (not actual) hourly pay for HITs selected (hereafter referred to as “advertised hourly pay” and the “advertised pay gap”). The average task was advertised to pay $4.88 per hour (95% CI $4.69, $5.10).

The pattern across demographic characteristics shows that the advertised hourly pay gap between genders is pervasive. Notably, a significant advertised gender pay gap is evident in every level of each covariate considered in Table 4 , but more pronounced among some subgroups of workers. For example, the advertised pay gap was highest among the youngest workers ($0.31 per hour for workers age 18–29), and decreased linearly with age, declining to $0.13 per hour among workers age 60+. Advertised houry gender pay gaps were evident across all levels of education and income considered.

To further examine the potential influence of human capital factors on the advertised hourly pay gap, Table 5 presents the average advertised pay for selected tasks by level of experience on the CloudResearch platform. Workers were grouped into 4 experience levels, based on the number of prior HITs completed: Those who completed fewer than 100 HITs, between 100 and 500 HITs, between 500 and 1,000 HITs, and more than 1,000 HITs. A significant gender difference in advertised hourly pay was observed within each of these four experience groups. The advertised hourly pay for tasks selected by both male and female workers increased with experience, while the gender pay gap decreases. There was some evidence that male workers have more cumulative experience with the platform: 43% of male workers had the highest level of experience (previously completing 1,001–10,000 HITs) compared to only 33% of women.

Table 5 also explores the influence of task heterogeneity upon HIT selection and the gender gap in advertised hourly pay. K-means clustering was used to group HITs into 20 clusters initially based on the presence or absence of 5,140 distinct words appearing in HIT titles. Clusters with fewer than 50,000 completed tasks were then excluded from analysis. This resulted in 13 clusters which accounted for 94.3% of submitted work assignments (HITs).

The themes of all clusters as well as the average hourly advertised pay for men and women within each cluster are presented in the second panel of Table 5 . The clusters included categories such as Games, Decision making, Product evaluation, Psychology studies, and Short Surveys. We did not observe a gender preference for any of the clusters. Specifically, for every cluster, the proportion of males was no smaller than 46.6% (consistent with the slightly lower proportion of males on the platform, see Table 1 ) and no larger than 50.2%. As shown in Table 5 , the gender pay gap was observed within each of the clusters. These results suggest that residual task heterogeneity, a proxy for occupational segregation, is not likely to contribute to a gender pay gap in this market.

Task length was defined as the advertised estimated duration of a HIT. Table 6 presents the advertised hourly gender pay gaps for five categories of HIT length, which ranged from a few minutes to over 1 hour. Again, a significant advertised hourly gender pay gap was observed in each category.

Finally, we conducted additional supplementary analyses to determine if other plausible factors such as HIT timing could account for the gender pay gap. We explored temporal factors including hour of the day and day of the week. Each completed task was grouped based on the hour and day in which it was completed. A significant advertised gender pay gap was observed within each of the 24 hours of the day and for every day of the week demonstrating that HIT timing could not account for the observed gender gap (results available in Supplementary Materials).

In this study we examined the gender pay gap on an anonymous online platform across an 18-month period, during which close to five million tasks were completed by over 20,000 unique workers. Due to factors that are unique to the Mechanical Turk online marketplace–such as anonymity, self-selection into tasks, relative homogeneity of the tasks performed, and flexible work scheduling–we did not expect earnings to differ by gender on this platform. However, contrary to our expectations, a robust and persistent gender pay gap was observed.

The average estimated actual pay on MTurk over the course of the examined time period was $5.70 per hour, with the gender pay differential being 10.5%. Importantly, gig economy platforms differ from more traditional labor markets in that hourly pay largely depends on the speed with which tasks are completed. For this reason, an analysis of gender differences in actual earned pay will be affected by gender differences in task completion speed. Unfortunately, we were not able to directly measure the speed with which workers complete tasks and account for this factor in our analysis. This is because workers have the ability to accept multiple HITs at the same time and multiple HITs can sit dormant in a queue, waiting for workers to begin to work on them. Therefore, the actual time that many workers spend working on tasks is likely less than what is indicated in the metadata available. For this reason, the estimated average actual hourly rate of $5.70 is likely an underestimate and the gender gap in actual pay cannot be precisely measured. We infer however, by the residual gender pay gap after accounting for other factors, that as much as 57% (or $.32) of the pay differential may be attributable to task completion speed. There are multiple plausible explanations for gender differences in task completion speed. For example, women may be more meticulous at performing tasks and, thus, may take longer at completing them. There may also be a skill factor related to men’s greater experience on the platform (see Table 5 ), such that men may be faster on average at completing tasks than women.

However, our findings also revealed another component of a gender pay gap on this platform–gender differences in the selection of tasks based on their advertised pay. Because the speed with which workers complete tasks does not impact these estimates, we conducted extensive analyses to try to explain this gender gap and the reasons why women appear on average to be selecting tasks that pay less compared to men. These results pertaining to the advertised gender pay gap constitute the main focus of this study and the discussion that follows.

The overall advertised hourly pay was $4.88. The gender pay gap in the advertised hourly pay was $0.28, or 5.8% of the advertised pay. Once a gender earnings differential was observed based on advertised pay, we expected to fully explain it by controlling for key structural and individual-level covariates. The covariates that we examined included experience, age, income, education, family composition, race, number of children, task length, the speed of accepting a task, and thirteen types of subtasks. We additionally examined the time of day and day of the week as potential explanatory factors. Again, contrary to our expectations, we observed that the pay gap persisted even after these potential confounders were controlled for. Indeed, separate analyses that examined the advertised pay gap within each subcategory of the covariates showed that the pay gap is ubiquitous, and persisted within each of the ninety sub-groups examined. These findings allows us to rule out multiple mechanisms that are known drivers of the pay gap in traditional labor markets and other gig economy marketplaces. To our knowledge this is the only study that has observed a pay gap across such diverse categories of workers and conditions, in an anonymous marketplace, while simultaneously controlling for virtually all variables that are traditionally implicated as causes of the gender pay gap.

Individual-level factors

Individual-level factors such as parental status and family composition are a common source of the gender pay gap in traditional labor markets [ 15 ] . Single mothers have previously been shown to have lower reservation wages compared to other men and women [ 21 ]. In traditional labor markets lower reservation wages lead single mothers to be willing to accept lower-paying work, contributing to a larger gender pay gap in this group. This pattern may extend to gig economy markets, in which single mothers may look to online labor markets as a source of supplementary income to help take care of their children, potentially leading them to become less discriminating in their choice of tasks and more willing to work for lower pay. Since female MTurk workers are 20% more likely than men to have children (see Table 1 ), it was critical to examine whether the gender pay gap may be driven by factors associated with family composition.

An examination of the advertised gender pay gap among individuals who differed in their marital and parental status showed that while married workers and those with children are indeed willing to work for lower pay (suggesting that family circumstances do affect reservation wages and may thus affect the willingness of online workers to accept lower-paying online tasks), women’s hourly pay is consistently lower than men’s within both single and married subgroups of workers, and among workers who do and do not have children. Indeed, contrary to expectations, the advertised gender pay gap was highest among those workers who are single, and among those who do not have any children. This observation shows that it is not possible for parental and family status to account for the observed pay gap in the present study, since it is precisely among unmarried individuals and those without children that the largest pay gap is observed.

Age was another factor that we considered to potentially explain the gender pay gap. In the present sample, the hourly pay of older individuals is substantially lower than that of younger workers; and women on the platform are five years older on average compared to men (see Table 1 ). However, having examined the gender pay gap separately within five different age cohorts we found that the largest pay gap occurs in the two youngest cohort groups: those between 18 and 29, and between 30 and 39 years of age. These are also the largest cohorts, responsible for 64% of completed work in total.

Younger workers are also most likely to have never been married or to not have any children. Thus, taken together, the results of the subgroup analyses are consistent in showing that the largest pay gap does not emerge from factors relating to parental, family, or age-related person-level factors. Similar patterns were found for race, education, and income. Specifically, a significant gender pay gap was observed within each subgroup of every one of these variables, showing that person-level factors relating to demographics are not driving the pay gap on this platform.

Experience is a factor that has an influence on the pay gap in both traditional and gig economy labor markets [ 20 ] . As noted above, experienced workers may be faster and more efficient at completing tasks in this platform, but also potentially more savvy at selecting more remunerative tasks compared to less experienced workers if, for example, they are better at selecting tasks that will take less time to complete than estimated on the dashboard [ 20 ]. On MTurk, men are overall more experienced than women. However, experience does not account for the gender gap in advertised pay in the present study. Inexperienced workers comprise the vast majority of the Mechanical Turk workforce, accounting for 67% of all completed tasks (see Table 5 ). Yet within this inexperienced group, there is a consistent male earning advantage based on the advertised pay for tasks performed. Further, controlling for the effect of experience in our models has a minimal effect on attenuating the gender pay gap.

Another important source of the gender pay gap in both traditional and gig economy labor markets is task heterogeneity. In traditional labor markets men are disproportionately represented in lucrative fields, such as those in the tech sector [ 23 ]. While the workspace within MTurk is relatively homogeneous compared to the traditional labor market, there is still some variety in the kinds of tasks that are available, and men and women may have been expected to have preferences that influence choices among these.

To examine whether there is a gender preference for specific tasks, we systematically analyzed the textual descriptions of all tasks included in this study. These textual descriptions were available for all workers to examine on their dashboards, along with information about pay. The clustering algorithm revealed thirteen categories of tasks such as games, decision making, several different kinds of survey tasks, and psychology studies.We did not observe any evidence of gender preference for any of the task types. Within each of the thirteen clusters the distribution of tasks was approximately equally split between men and women. Thus, there is no evidence that women as a group have an overall preference for specific tasks compared to men. Critically, the gender pay gap was also observed within each one of these thirteen clusters.

Another potential source of heterogeneity is task length. Based on traditional labor markets, one plausible hypothesis about what may drive women’s preferences for specific tasks is that women may select tasks that differ in their duration. For example, women may be more likely to use the platform for supplemental income, while men may be more likely to work on HITs as their primary income source. Women may thus select shorter tasks relative to their male counterparts. If the shorter tasks pay less money, this would result in what appears to be a gender pay gap.

However, we did not observe gender differences in task selection based on task duration. For example, having divided tasks into their advertised length, the tasks are preferred equally by men and women. Furthermore, the shorter tasks’ hourly pay is substantially higher on average compared to longer tasks.

Additional evidence that scheduling factors do not drive the gender pay gap is that it was observed within all hourly and daily intervals (See S1 and S2 Tables in Appendix). These data are consistent with the results presented above regarding personal level factors, showing that the majority of male and female Mechanical Turk workers are single, young, and have no children. Thus, while in traditional labor markets task heterogeneity and labor segmentation is often driven by family and other life circumstances, the cohort examined in this study does not appear to be affected by these factors.

Practical implications of a gender pay gap on online platforms for social and behavioral science research

The present findings have important implications for online participant recruitment in the social and behavioral sciences, and also have theoretical implications for understanding the mechanisms that give rise to the gender pay gap. The last ten years have seen a revolution in data collection practices in the social and behavioral sciences, as laboratory-based data collection has slowly and steadily been moving online [ 16 , 24 ]. Mechanical Turk is by far the most widely used source of human participants online, with thousands of published peer-reviewed papers utilizing Mechanical Turk to recruit at least some of their human participants [ 25 ]. The present findings suggest both a challenge and an opportunity for researchers utilizing online platforms for participant recruitment. Our findings clearly reveal for the first time that sampling research participants on anonymous online platforms tends to produce gender pay inequities, and that this happens independent of demographics or type of task. While it is not clear from our findings what the exact cause of this inequity is, what is clear is that the online sampling environment produces similar gender pay inequities as those observed in other more traditional labor markets, after controlling for relevant covariates.

This finding is inherently surprising since many mechanisms that are known to produce the gender pay gap in traditional labor markets are not at play in online microtasks environments. Regardless of what the generative mechanisms of the gender pay gap on online microtask platforms might be, researchers may wish to consider whether changes in their sampling practices may produce more equitable pay outcomes. Unlike traditional labor markets, online data collection platforms have built-in tools that can allow researchers to easily fix gender pay inequities. Researchers can simply utilize gender quotas, for example, to fix the ratio of male and female participants that they recruit. These simple fixes in sampling practices will not only produce more equitable pay outcomes but are also most likely advantageous for reducing sampling bias due to gender being correlated with pay. Thus, while our results point to a ubiquitous discrepancy in pay between men and women on online microtask platforms, such inequities have relatively easy fixes on online gig economy marketplaces such as MTurk, compared to traditional labor markets where gender-based pay inequities have often remained intractable.

Other gig economy markets

As discussed in the introduction, a gender wage gap has been demonstrated on Uber, a gig economy transportation marketplace [ 20 ], where men earn approximately 7% more than women. However, unlike in the present study, the gender wage gap on Uber was fully explained by three factors; a) driving speed predicted higher wages, with men driving faster than women, b) men were more likely than women to drive in congested locations which resulted in better pay, c) experience working for Uber predicted higher wages, with men being more experienced. Thus, contrary to our findings, the gender wage gap in gig economy markets studied thus far are fully explained by task heterogeneity, experience, and task completion speed. To our knowledge, the results presented in the present study are the first to show that the gender wage gap can emerge independent of these factors.

Generalizability

Every labor market is characterized by a unique population of workers that are almost by definition not a representation of the general population outside of that labor market. Likewise, Mechanical Turk is characterized by a unique population of workers that is known to differ from the general population in several ways. Mechanical Turk workers are younger, better educated, less likely to be married or have children, less likely to be religious, and more likely to have a lower income compared to the general United States population [ 24 ]. The goal of the present study was not to uncover universal mechanisms that generate the gender pay gap across all labor markets and demographic groups. Rather, the goal was to examine a highly unique labor environment, characterized by factors that should make this labor market immune to the emergence of a gender pay gap.

Previous theories accounting for the pay gap have identified specific generating mechanisms relating to structural and personal factors, in addition to discrimination, as playing a role in the emergence of the gender pay gap. This study examined the work of over 20,000 individuals completing over 5 million tasks, under conditions where standard mechanisms that generate the gender pay gap have been controlled for. Nevertheless, a gender pay gap emerged in this environment, which cannot be accounted for by structural factors, demographic background, task preferences, or discrimination. Thus, these results reveal that the gender pay gap can emerge—in at least some labor markets—in which discrimination is absent and other key factors are accounted for. These results show that factors which have been identified to date as giving rise to the gender pay gap are not sufficient to explain the pay gap in at least some labor markets.

Potential mechanisms

While we cannot know from the results of this study what the actual mechanism is that generates the gender pay gap on online platforms, we suggest that it may be coming from outside of the platform. The particular characteristics of this labor market—such as anonymity, relative task homogeneity, and flexibility—suggest that, everything else being equal, women working in this platform have a greater propensity to choose less remunerative opportunities relative to men. It may be that these choices are driven by women having a lower reservation wage compared to men [ 21 , 26 ]. Previous research among student populations and in traditional labor markets has shown that women report lower pay or reward expectations than men [ 27 – 29 ]. Lower pay expectations among women are attributed to justifiable anticipation of differential returns to labor due to factors such as gender discrimination and/or a systematic psychological bias toward pessimism relative to an overly optimistic propensity among men [ 30 ].

Our results show that even if the bias of employers is removed by hiding the gender of workers as happens on MTurk, it seems that women may select lower paying opportunities themselves because their lower reservation wage influences the types of tasks they are willing to work on. It may be that women do this because cumulative experiences of pervasive discrimination lead women to undervalue their labor. In turn, women’s experiences with earning lower pay compared to men on traditional labor markets may lower women’s pay expectations on gig economy markets. Thus, consistent with these lowered expectations, women lower their reservation wages and may thus be more likely than men to settle for lower paying tasks.

More broadly, gender norms, psychological attributes, and non-cognitive skills, have recently become the subject of investigation as a potential source for the gender pay gap [ 3 ], and the present findings indicate the importance of such mechanisms being further explored, particularly in the context of task selection. More research will be required to explore the potential psychological and antecedent structural mechanisms underlying differential task selection and expectations of compensation for time spent on microtask platforms, with potential relevance to the gender pay gap in traditional labor markets as well. What these results do show is that pay discrepancies can emerge despite the absence of discrimination in at least some circumstances. These results should be of particular interest for researchers who may wish to see a more equitable online labor market for academic research, and also suggest that novel and heretofore unexplored mechanisms may be at play in generating these pay discrepancies.

A final note about framing: we are aware that explanations of the gender pay gap that invoke elements of women’s agency and, more specifically, “choices” risk both; a) diminishing or distracting from important structural factors, and b) “naturalizing” the status quo of gender inequality [ 30 ] . As Connor and Fiske (2019) argue, causal attributions for the gender pay gap to “unconstrained choices” by women, common as part of human capital explanations, may have the effect, intended or otherwise, of reinforcing system-justifying ideologies that serve to perpetuate inequality. By explicitly locating women’s economic decision making on the MTurk platform in the broader context of inegalitarian gender norms and labor market experiences outside of it (as above), we seek to distance our interpretation of our findings from implicit endorsement of traditional gender roles and economic arrangements and to promote further investigation of how the observed gender pay gap in this niche of the gig economy may reflect both broader gender inequalities and opportunities for structural remedies.

Supporting information

Funding statement.

The authors received no specific funding for this work.

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Mind the gap: exploring the impact of the gender wage gap towards women's academic success and career aspirations

• Masters Thesis
• Gervacio, Alyzza Colleen A.
• Valiquette L'Heureux, Anais
• David, Ariane
• Political Science
• California State University, Northridge
• Dissertations, Academic -- CSUN -- Political Science.
• Gender Wage Gap
• Higher Education
• Glass Ceiling
• Educational Attainment
• Devaluation
• Occupational Segregation
• Human Capital
• http://hdl.handle.net/20.500.12680/sb397g94d
• by Alyzza Colleen Gervacio

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Gender-Specific Wage Structure and the Gender Wage Gap in the U.S. Labor Market

• Original Research
• Open access
• Published: 21 November 2022
• Volume 165 , pages 585–606, ( 2023 )

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This paper challenges the predominant conceptualization of the wage structure as gender-neutral, emphasizing the contribution that this makes to the gender wage gap. Unlike most decomposition analyses, which concentrated on gender differences in productivity-enhancing characteristics (the ‘explained’ portion), we concentrate on the ‘wage structure’ (the ‘unexplained’ portion), which can be defined as the market returns to productivity-enhancing characteristics. These returns are commonly considered a reflection of non-gendered economic forces of supply and demand, and gender differences in these returns are attributed to market failure or measurement error. Using PSID data on working-age employees from 1980 to 2010, we examine gender differences in returns to education and work experience in the U.S. labor market. Based on a threefold decomposition, we estimate the contribution of these differences to the overall pay gap. The results show that men’s returns to education and work experience are higher than women’s; and that in contrast to the well-documented trend of narrowing gender gaps in skills and earnings, the gaps in returns increase over time in men’s favor. Furthermore, the existing gender differences in returns to skills explain a much larger proportion of the gender wage gap than differences in levels of education and experience between men and women. The paper discusses the mechanisms underlying these findings.

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1 Introduction

The gender wage gap is the subject of an extensive and rich body of literature. Various theoretical arguments, backed by empirical evidence, have been presented over time to explain the sources of the gender wage gap, and its changes over time. A central strand of this literature concentrates on estimating the contribution of different factors to the overall gender wage gap. Studies in this line of research decompose the gender gap to its constituent components, while making a distinction between the ‘explained’ and the ‘unexplained’ portions of the gender pay gap (Kunze, 2008 ). The ‘explained’ portion refers to the part of the gap that is the consequence of differences between men and women in their productivity-related characteristics, such as work experience and education. The ‘unexplained’ portion is the ‘residual’, commonly presented as a rough estimate of labor market discrimination. While the ‘explained’ component of the gap has been meticulously scrutinized, the ‘unexplained’ portion is usually treated as a monolithic element, and as such is rarely given close and detailed examination despite its increasing relative size over time.

In this study, we focus on the ‘unexplained’ portion of the gender wage gap, a focus that carries theoretical and empirical contributions. Empirically, we do not leave the ‘unexplained’ portion of the wage gap as a solid irreducible block; rather, we disaggregate it to its two main components, education and work experience. This approach is uncommon in the field. The extensive literature on the gender wage gap focuses almost entirely on the ‘explained’ portion of the gap in examining gender differences in education and experience—namely differences at the individual level. Studies that decompose the gender wage gap document the importance of such differences at the individual level—especially the convergence between men and women with regard to education and experience—in explaining the decline in the gender pay gap over time (Blau & Kahn, 1997 , 2017a ; Kunze, 2017 ). The structural aspects of the gender wage gap, on the other hand, are often left unnoticed.

Theoretically, we stress that the ‘unexplained’ portion is gender specific, in contrast to the bulk of the existing research, which conceptualizes it as gender-neutral. The ‘unexplained’ portion of the wage gap relates to structural aspects of gender inequality which operate beyond the individual level, and therefore are harder to identify. This structural portion refers to the market price for productivity-enhancing skills, arguably determined by supply and demand and thus generally assumed to be gender-neutral (Blau & Kahn, 1996 , 1997 ). Following this assumption, if the market price is not identical for men and women, it is assumed to be due to market failure (i.e., discrimination) or measurement error and misspecification. In contrast to this, the present study is motivated by the notion that structural aspects are gender-specific rather than gender-neutral; accordingly, our goal is to demonstrate how men and women are rewarded differently for their skills.

In this paper, then, we focus on the ‘unexplained’ portion of the gender wage gap, i.e., the market monetary rewards to skills, and its changing size between 1980 and 2010. Specifically, we examine how market rewards to the two prime human-capital determinants of wages—education and work experience—differ for men and women, and the extent to which these differences contribute to the gender pay gap. Since gender differences in productivity-enhancing skills have dramatically changed over the last few decades, alongside changes to the returns to these skills, we also examine how the ‘unexplained’ portion of the wage gap changes over time.

Our findings show that market rewards to education and work experience differ significantly for men and women, and that these differences account for a much larger proportion of the wage gap than differences in the levels of education and work experience between men and women. Furthermore, not only is it that this ‘explained’ portion of the gender gap smaller; its significance is in decline (see also Blau & Kahn, 2017a , 2017b ), while structural aspects (the ‘unexplained’ portion) remain a powerful force behind the persistence of the gender wage gap. The durable importance of the ‘unexplained’ portion of the gender pay gap means that further improvements in women’s human capital will hardly contribute to minimizing their wage disadvantage, as their inferior wage is mainly the result of the lower evaluation of their skills. The conclusions that stem from these findings are rather clear, and should be echoed to both researchers and policy makers: women’s investment in human capital will only contribute to minimizing their economic inferiority when the ‘wage structure’—the criteria underpinning the market prices for skills and occupations—is equal for men and women. For as long as the market continues to reward men and women with equivalent skills differently, the gender gap will persist.

The paper is structured as follows: Sect.  2 critically reviews the common conceptualization of the wage structure, Sect.  3 discusses methods of decomposition of the gender wage gap and their ability to identify the contribution of the wage structure to the pay gap, Sect.  4 presents the data and variables used in the analysis, Sect.  5 presents the findings, and finally, Sect.  6 draws the conclusions and proposes the mechanisms underlying the results.

2 Individual and Structural Aspects of the Gender Wage Gap

The literature explaining earnings inequality between male and female workers relies heavily on Becker’s human capital theory (Becker, 1962 ) and Mincer’s earnings function (Mincer, 1974 ) that serve as the cornerstone for understanding wage differentials in modern labor markets. In a nutshell, the human capital theory suggests that economic rewards are primarily based on ‘productivity’, and hence workers with higher productivity-enhancing characteristics will earn higher wages. Education and work experience consist the two main components of human capital, and thus have been studied extensively.

Applying this model for studying wage differences between men and women means that, theoretically, the gender wage gap (or at least a significant part of it) is a reflection of the fact that men and women entering the labor force differ in their human capital. In other words: if men generally have higher productive skills than women, this will result in wage disparities in their favor. In case of a convergence between men and women in human capital, a similar convergence in the gender wage gap should be expected as well. According to this logic, studies that decompose the gender wage gap term the portion of the gap attributed to differences in human capital between men and women the ‘explained’ portion. However, differences in human capital and other productivity-related characteristics do not explain the entire gap. The gender wage gap is also an outcome of the market price for human capital, a price that is affected by supply and demand. The market price for skills constitutes the ‘ wage structure ’, or, in other words, what employers are willing to pay for certain productivity-enhancing qualities (Blau & Kahn, 1996 , 1997 ).

In theory, the wage structure—as the ‘real’ market value for productivity-related skills—is gender-neutral, determined by economic forces in a given market regardless of the worker’s identity. Following this assumption, if gender differences in the rewards for skills do exist it is due to market failure; therefore, these differences are conceived as the ‘unexplained’ portion of the gender wage gap. Indeed, the ‘unexplained’ portion is often presented as an estimate of ‘market discrimination’, which in decomposition reasearch relates to unequal pay for workers of different genders with the same productivity-related skills, i.e., based on their gender rather than their productivity.

Although the wage structure is assumed to be gender-neutral, in a series of seminal works Blau and Kahn brought attention to the importance of the ‘wage structure’ in shaping the gender wage gap. They showed that even if we assume (as they did) that the wage structure is gender-neutral, i.e., the market price for skills is identical for men and women, it can still affect the gender pay gap so long as men’s productivity-related characteristics are superior to those of women. Work experience, one of the main components of human capital, is an excellent example. Women lag behind men in terms of labor market experience, such that even if the rewards for each year of experience are equal for men and for women, men will benefit more than women from work experience simply because they have more of it. As the rewards for experience are higher, so too is the advantage enjoyed by men. Furthermore, if women’s average experience rises (a change at the individual level), the gender wage gap can be expected to narrow. But, if at the same time the wage structure changes such that the returns to experience grow substantially (a change at the structural level), men’s advantage in terms of experience, while shrinking, can still hold the wage gap from decreasing because men benefit more from their remaining advantage (Blau & Kahn, 1997 , 2017a ).

The effect of the wage structure, according to this logic, is not due to gender differences in rewards to wage-related characteristics (which are assumed to be the same), but due to compositional differences between men and women with regard to wage-related characteristics. Thus, Blau and Kahn, as well as others, have concentrated on the ‘explained’ portion of the gap, i.e., differences berween men and women in key wage-related characteristics such as education and experience. The ‘unexplained’ portion, in contrast, which has been attributed to market failure (discrimination) or measurment error, remained an aggregated unified unit, reflecting the assumption that the wage structure is basically gender-neutral.

The conceptualization of the wage structure as gender-neutral fails to account for systematic differences between the genders in returns to productivity-related characteristics. Because the labor market is embedded in the broader power relations of any given society, we cannot accept the assumption that men and women are subject to the same ‘criteria’ that set workers’ wages; rather, the basic presumption should be that the price that employers are willing to pay for certain productivity-enhancing qualities differ for men and for women. Our empirical motivation for disaggregating this structural component of the gender wage gap is therefore based on our theoretical argument that these ‘criteria’ are gender-specific, rather than gender-neutral. By that, we point at the direct effect of the wage structure, above and beyond its indirect effect via the compositional differences described above. In practical terms, this means that unlike most decomposition analyses, we aim to disaggregate the ‘unexplained’ portion of the wage gap, in order to reveal how gender differences in returns to the two prime components of human capital—education and work experience—affect the gender wage gap.

This is not the first attempt to follow this direction. The idea that wage structures are in fact gender-specific is supported by earlier studies which provide evidence for gender differences in returns to education, work experience, and occupations. These studies differ from ours in their theoretical motivation as well as their empirical analysis. Studies by Dougherty ( 2005 ), Goldin ( 2014 ), and Munasinghe et al. ( 2008 ) examined gender differences in returns to human capital, but did not examine the contribution of these differences to the gender wage gap. In contrast, studies that did examine the contribution of structural aspects to the gender wage gap (e.g., Albrecht et al., 2003 ; Arulampalam et al., 2007 ; Chzhen & Mumford, 2011 ; Filer, 1985 ) did not look further in order to distinguish between differences in returns to specific characteristics like education and experience, as we do. The rising returns to both education and experience over recent decades (Haelermans & Borghans, 2012 ; Juhn et al., 1993 ; Murphy & Topel, 2016 ) makes the distinction between the two even more crucial for understanding trends in the gender wage gap.

3 Decomposition of the Gender Wage Gap

When shifting the focus from gender differences in human capital to gender differences in returns to human capital, the standard way of decomposing the gender wage gap has crucial limitations. We propose two deviations from the standard method: put together, these will facilitate accounting for the substantive differences in returns to skills between male and female employees.

There is a long tradition of research using the Oaxaca-Blinder (Blinder, 1973 ; R. Oaxaca, 1973 ) decomposition technique to examine how much of the gender wage gap is explained by differences in productive characteristics between men and women (Lips, 2013 ; Ponthieux & Meurs, 2015 ). The starting point of this analysis is the Mincerian earnings function, which regresses the log-wage against measures of individual human capital—namely actual work experience and education—and other background variables (Kunze, 2008 ). The decomposition is based on estimating the earnings function for men and women separately, and then using these estimates in a counterfactual analysis which considers what would happen to the gender wage gap if women had the same characteristics as men. The decrease of the wage gap in such circumstances is considered the ‘explained portion’ of the gap, or the ‘endowments effect’. The residual is attributed to unmeasured variables and discrimination. More formally, the standard decomposition, also known as the twofold decomposition, can be written as:

where W is the hourly wage, X denotes measures of human capital, and β is the returns to human capital estimated by the male and female wage equations, labelled m and f respectively.

There are two problems with this widely used formulation. The first problem arises from the implications of using semi-log models in which the logarithms of wages are estimated. The coefficients in a semi-log model do not represent the absolute marginal effect of the covariates on the dependent variable, but rather the relative effect (i.e., the percentage change in the dependent variable due to an absolute change in the covariates). In the case of the earnings function, this means that the coefficients serve as estimates of the rate of return to investment in human capital, and do not represent the monetary gain with each unit increase in the covariates. The rate of return fits the neoclassical economic perspective, which conceives human capital in terms of an investment made by individuals, and the benefits that accompany human capital as returns to investment. According to this perspective, the rational decision regarding whether to invest in education, for example, depends on the expected benefit, given your gender and your expected earnings otherwise.

Because the coefficients in a semi-log model represent the percentage—rather than absolute—change in the dependent variable, the coefficients are not comparable across gender groups. The relative returns estimated in the male equation are relative to the male distribution of wages, while those in the female equation are relative to the female distribution. Because the baselines are different for the two gender distributions, a 10% wage increase in the women’s distribution is worth less (in absolute terms) than a 10% increase in the men’s distribution. To give a concrete example, Diprete and Buchmann ( 2006 ) used CPS data to estimate returns to education by comparing the earnings of college graduates and high school graduates. They found that in 2000, returns to college education was higher for women than for men; white women could expect a 118% increase in their earnings (relative to high-school graduate women), as compared to only 69% increase for men. However, this higher college premium is actually worth less than men’s in absolute terms: it translates to $10,959 premium, as opposed to $12,353 for men. Thus, comparisons of the relative returns to a specific skill provide important information on the benefit of that skill for men and women given their potential earnings otherwise; but little information on gender inequality in returns to that specific skill.

The unchallenged convention of always using the log-wage in statistical models, together with the predominant conceptualization by economists of human capital as an individual’s investment, has led to the misrepresentation of the gender gap in returns to education and experience—even in studies that were focused on precisely this. The comparison of rates of return between men and women can tell us which gender group has a stronger incentive to invest in acquiring human capital given its potential earnings otherwise; but it does not inform us about gender differences in the real value of these investments (Pekkarinen, 2012 ). Since unskilled women tend to earn much lower wages than unskilled men, the relative returns of each gender are calculated from very different baselines. Indeed, the findings of Diprete and Buchmann ( 2006 ) cited above show that the within-group relative returns to college education were higher for women than for men; but this does not mean that men get less for their education. Even if the relative returns to skills were exactly the same—a state of affairs that can be defined as zero-discrimination conditions—the absolute wage increase associated with these skills may still be substantially different. What should be compared, then, are the absolute returns.

The second problem with the standard decomposition arises from the fact that it adopts the coefficients from the male earnings function as representative of the wage structure. Thus, the counterfactual scenario that is being tested is one in which men and women have equal skills and are rewarded for them as men currently are. This issue has long been recognized, and revisions to this basic application use ‘middle-ground’ coefficients representing a nondiscriminatory wage structure (R. L. Cotton, 1988 ; Neumark, 1988 ; Oaxaca & Ransom, 1994 ). Either way, the choice of male coefficients, female coefficients, or midpoint coefficients as the reference point assumes a uniform gender-neutral wage structure, which cannot account for the possibility that rewards to human capital are gender-specific. In other words, it does not allow for estimating the contribution of gender-specific wage structures to the gender wage gap. Furthermore, a decomposition that relies on a standard monolithic wage structure (by using the men's, women's, or midpoint coefficients) contaminates the ‘explained’ portion of the wage gap with the deviations from this standard. Part of the ‘explained’ portion is not due to differences in skills alone, but actually due to differences in coefficients (Elder et al., 2010 ). For example, if the male coefficients are used as the standard (as in the equation above), the ‘explained’ portion of the gap consists of the part of the gap that is due to differences in human capital between men and women if women had men’s coefficients – which they most likely do not. This method, thus, overestimates the ‘explained’ portion of the wage gap, and downplays the contribution of the structural component, i.e., the differences in returns to human capital.

What we propose as a solution to the problems raised above is, first, to examine the gender wage gap in absolute—rather than relative—terms; and second, to use a threefold—rather than a twofold—decomposition. As noted, the use of the absolute wage as the dependent variable renders the coefficients comparable between men and women, as they are not dependent on men’s and women’s different baselines (the constants in the male and female wage equations). Therefore, the gap between men’s and women's coefficients represents gender inequality in the value of labor market skills. The threefold decomposition (Jann, 2008 ) allows isolating the contribution of differences in the covariates and the contribution of differences in the coefficients of each covariate to the overall wage gap. It divides the wage gap into three parts: (1) the endowments effect, i.e., the part explained by differences in men’s and women’s characteristics, but this time without changing the returns that women currently receive; (2) the 'wage structure' i.e., the part explained by differences in returns/market prices to these characteristics; and (3) the interaction between differences in endowments and coefficients. Formally, it can be written as:

This threefold decomposition allows assessing separately what would happen to the wage gap if women had the same characteristics as men but kept their current returns; and what would happen if women were rewarded for their current characteristics equally to men. Since gender differences in characteristics have declined over time, we can expect that the role of these differences in explaining the wage gap should decline as well.

4 Analysis and Data

Our analysis and data are motivated from and based on a recent publication by Francine Blau and Lawrence Kahn (Blau & Kahn, 2017a ), which provides an excellent and comprehensive summary of the research, and an updated empirical account of the gender wage gap in the United States. As discussed above, we employ two modifications to the standard decomposition method used by Blau and Kahn ( 2017a ): estimating the absolute, rather than relative, effects of human capital; and using a threefold, rather than twofold, decomposition. These modifications allow us to present an estimate of the real economic rewards to human capital received by men and women, and then to estimate the part of the wage gap that can be attributed to the differences in these rewards. Unlike previous studies, we distinguish between the rewards for education and work experience, in order to better understand the effect of the wage structure on the gender wage gap. Lastly, we conduct this analysis at two points in time, in order to capture changes in the role of the gender-specific wage structure over time.

4.1 Data and Variables

We analyze the gender wage gap at two time points, 1980 and 2010. Blau and Kahn’s analysis and findings serve as the reference point for our empirical analysis. For consistency, we use the same data, same variables, and same specification as in their human capital specification model (ibid, Table 4, panel A), in which the hourly wage is regressed on education and work experience, controlling for race and region. Footnote 1 Blau and Kahn used data from the Michigan Panel Study of Income Dynamics (PSID), which is the only dataset available with detailed information on the actual work experience Footnote 2 of respondents from all cohorts. We use the same dataset, which they have made available online (Blau & Kahn, 2017b ). Following Blau and Kahn, we focus on working-age (25–64) non-farm employees who worked full-time for at least 26 weeks during the year preceding the survey. This focus allows comparing men and women with similar levels of commitment to the labor market.

The dependent variable in the analysis is the inflation-adjusted hourly wage. The independent variables include the following: 1) Gender ; 2) Education— measured by two variables, years of schooling and academic degree (in three categories: no degree, BA, and postgraduate degree); 3) Work experience— we follow Blau and Kahn and others in differentiating between experience in full-time work, which is associated with positive returns, and experience in part-time work, which is indicative of low earnings and hence negative returns (Gornick & Jacobs, 1996 ; Olsen & Walby, 2004 ). The fact that the PSID data include information on the work history of each respondent is what puts them in a unique position to provide the most accurate measures of actual work experience, as opposed to the frequently used estimated experience based on age and years of schooling (Blau & Kahn, 2013 ); 4) Race—in four categories: white, black, Hispanic, and other; 5) Region – in four categories; and 6) residence in metropolitan vs. rural areas. Footnote 3

Figure  1 displays a narrowing of the gender gap in the two prime components of human capital, average years of schooling and full-time work experience. In 1980, women had a slightly lower average number of years of schooling compared to men (13.12 versus 13.37), but by 2010, the balance had been reversed (14.47 for women versus 14.33 for men). The difference in years of full-time work was much larger in 1980 (12.09 for women versus 20.05 for men) before narrowing substantially by 2010 (15.54 versus 17.68 for women and men, respectively). As discussed at the outset, gender differences in education and work experience constitute the ‘explained’ portion of the gender wage gap. As these differences converge over time, we can expect their explanatory power to decline.

Gender differences in mean years of schooling and full-time work experience

Blau and Kahn report that the women-to-men earnings ratio increased from 62% in 1980 to 79% in 2010. They followed the tradition of decomposing the log-wage differentials using the male coefficients, finding that differences in human capital between men and women explained 26.6% of the wage gap in 1980 and only 8% in 2010. Footnote 4 This should come as no surprise, given the growing educational attainments of women and the increase in the extent of their work experience. In contrast, the ‘unexplained’ portion of the gap grew from 71.4% in 1980 to 85.2% in 2010. Whilst Blau and Kahn acknowledge that the unexplained portion is the main source of gender inequality, not much of the discussion is dedicated to these differences in returns, and the contribution of specific elements of these differences, namely education and experience, are not reported. Nonetheless, these findings are important because they highlight the relatively small and declining importance of gender differences in human capital in explaining women’s lower wages, rebutting the claim that women’s wage disadvantage reflects their lower skills.

As discussed above, we have introduced two changes to Blau and Kahn’s analysis: we use the real hourly wage instead of its log transformation, and employ a threefold decomposition instead of its regular twofold form. We also differ from Blau and Kahn in our basic assumption that returns to human capital are gender specific; thus, our first step is to test whether differences in returns between men and women do exist. To this end, we begin by presenting findings from an OLS regression of hourly wages where education and experience are both interacted with gender, to examine whether and to what extent men’s and women’s coefficients differ. Note that although we focus on education and experience, the model also controls for Region and Race, replicating Blau and Kahn's human capital model. We conducted this analysis in two versions: using the log-wage in one and the real (inflation-adjusted) wage in the other, to concretize the difference between the rates of return (derived from the semi-log model) and the absolute returns (derived from the real wage model). The results of these analyses are presented in Table 1 , and the more substantial findings are outlined in Figs.  2 and 3 .

a Relative returns to education. b Absolute returns to education

a Relative returns to full-time work experience. b Absolute returns to full-time work experience

5.1 Gender Differences in Returns to Skills

Figure  2 A illustrates the effect of education (based on both degree and years of schooling) on wages, by gender and year. Note that in Fig.  2 a, returns are expressed as a percentage increase, as they are based on the semi-log models. The figure shows that between 1980 and 2010 both men’s and women’s lines moved upwards, reflecting the overall increase in returns to education. The figure suggests that women had higher relative returns to education in 1980 but had lost this advantage by 2010. Indeed, the first column of Table 1 displays positive interaction terms between education and gender, suggesting that in 1980 women (coded as 1) tended to have higher rates of return to their education. But this gender gap in returns to both BA education and years of schooling is only on the brink of statistical significance, Footnote 5 and it is only the interaction with postgraduate education that manifests statistical significance. Footnote 6 The combined effect of years of schooling and degree attainment, as presented in Fig.  2 a, can be comprehended more intuitively. The figure shows that in 1980 possessing a postgraduate degree and 18 years of schooling added 172% to the earnings of men (compared to men with no schooling) and 303% to the earnings of women. However, as Fig.  2 a clearly shows, these differences in returns had dramatically narrowed by 2010; possessing a postgraduate degree and 18 years of schooling added around 475% to the earnings of both men and women.

When the dependent variable is the absolute wage (in US$), a different picture emerges. The third and fourth columns of Table 1 present the results of the real wage models for 1980 and 2010 respectively, and Fig.  2 b illustrates the combined absolute effect of education on wages by gender and year. Here too, the overall increase in returns to education between 1980 and 2010 is evident. More importantly, the figure shows that in both years, women had lower absolute returns to their education, and that the gap increased considerably between 1980 and 2010, especially in the case of higher education. The marginal effects reported in Table 1 suggest that the hourly wage premium for a bachelor’s degree in 2010 was $12.3 for men, compared to only $6 for women. For advanced degrees, the gap increased further, with a $22 premium for men and $9.8 for women. At both levels, then, the premium for higher education was more than double for men – an important finding which is completely obscured when returns to education are expressed in relative terms. The contrast between Fig.  2 a and b is striking. Although both show that returns to education are gender specific, the latter shows a clear advantage for men, in all education categories and for both time-periods, which cannot be seen in the former, where education premiums are presented as rates of return.

Turning to experience, it is important to note that due to the nonlinear effect of experience (each year of experience has a stronger effect during the earlier stages of one’s career) the models include a quadric term of experience. As a consequence, it is harder to interpret the results from the table, especially since all the experience variables are also interacted with gender. To clearly demonstrate the effect of experience in both models, we plotted the wage premiums across the scale of full-time experience and we relate to the graphic representation. Figure  3 a presents the rates of return (as a percentage) and Fig.  3 b presents the absolute hourly wage premium, based on the log-wage and real wage models respectively. The two figures show, first, that the positive effect of experience on earnings is indeed nonlinear, as it weakens as we move towards the higher end of the scale. Second, the figures show that the increase and then decrease in returns at the beginning and at the end of a lifetime career (lower and then higher values of experience) are sharper among men in 2010. Third, between 1980 and 2010, both men’s and women’s lines moved upwards, reflecting the overall increase in returns to experience. Lastly, and most importantly for the current discussion, the figures show that in both years, men benefited from their experience substantially more than women, and the findings are similar whether it is relative or absolute returns being considered. Nevertheless, in the case of relative returns, the gender gap appears to have declined between 1980 and 2010, whereas the gender gap in absolute returns remained at a similar level.

The findings presented so far support our arguments in two ways. First, they illustrate the substantive difference between the rates of return estimated by the semi-log model and the absolute returns estimated by the real wage model, and show that they are not equivalent. To reiterate, the relative returns give us the percentage increase in wages due to education and work experience, based on each group’s distinctive distribution, and thus are informative for learning about the different incentives men and women have for investing in their human capital. They are not, however, useful for comparisons between groups. Second, the findings show that men and women do not receive the same returns to their productive skills. Women’s absolute returns to both education and experience are substantially lower than men’s, meaning that the market rewards to human capital are not gender-neutral.

5.2 Decomposing the Real Hourly Gender Wage Gap

These findings lead to the next step of our analysis: examining the contribution of gender differences in levels of education and experience ("explained"), versus the contribution of gender differences in returns to these skills ("unexplained"), to the gender wage gap. We do so by decomposing the real hourly wage gap between men and women in 1980 and 2010. Table 2 reports the results, and Fig.  4 illustrates the key findings from this analysis. Since our theoretical interest here is in a comparison of returns by gender, we only decompose the gender gaps in absolute wages.

Blinder-Oaxaca threefold decomposition of the gender wage gap (selected findings)

As shown in Table 2 , the estimated gap between men’s and women’s hourly wages was $9.2 in 1980, declining to $8.06 by 2010. As a threefold decomposition was used, the ‘explained’ portion of the gap represents only the differences in characteristics between men and women, and is isolated from the gender differences in returns. As expected, the contribution of the former to the wage gap is small: 12.1% in 1980, and only 2.7% in 2010. This small effect indicates how important it is to pay much more attention to the ‘unexplained’ portion of the gap. More specifically, in 1980 gender differences in educational attainments explained 3.7% of the wage gap. In 2010 women’s level of education was already higher than men’s. Therefore, education had a suppressing effect on the wage gap; equalizing women’s education level to that of men’s would have increased the wage gap by 5.2%. In both years, then, education differences either had a small effect on the wage gap or obscured it.

The results concerning experience are similar. Women, on average, have less experience than men; while the gap shrunk over time, they still lag behind (see Fig.  1 ). Accordingly, this difference explained 7.3% of the wage gap in 1980, and even less—6.3%—in 2010. These figures are smaller than those reported by Blau and Kahn (23.9% and 15.9% in 1980 and 2010, respectively), indicating that the standard twofold decomposition contaminates the 'explained' effect with the effect of the returns differential; while simultaneously failing to correctly account for the substantive differences between men’s and women’s returns, due to its reliance on log-wage equations. In a threefold decomposition of the log-wage gap (not presented here), distinguishing between the effect of different characteristics and different rates of return, we found that the portion explained by experience was 13.3% and 9.2% in 1980 and 2010, respectively. These figures are much closer to the findings from the decomposition of the real wage gap than to Blau and Kahn’s original (log-wage) findings—pointing again at the necessity of isolating the effect of the market returns to skills (i.e., the gendered wage structure) from the effect of differences in human capital.

In contrast to the effect of differences in human capital, then, differences in returns to human capital played a much larger and increasing role in explaining the gender gap, as Fig.  4 clearly shows. If women had the same returns to education as men, the wage gap would have narrowed by 45.4% in 1980 and 47.6% in 2010. An even larger portion of the wage gap is attributed to differences in returns to experience. Specifically, if women had the same returns to experience as men, the wage gap would have narrowed by 66.2% in 1980, and 86.4% in 2010. As gender differences in human capital are diminishing while gender differences in returns persist or even increase, it is unsurprising to find that the gender wage gap is largely shaped by the latter.

Two interesting points arise from the findings, pointing at the interdependence of the factors that are at play. With regard to work experience, the portion of the gender wage gap that is due to differences in returns to experience increased substantially, even though gender differences in these returns were similar in 1980 and 2010 (see Fig.  3 b). This conundrum can be resolved if we take into account the fact that returns to experience rose considerably between the two periods, and therefore had much greater weight in 2010, even if the differences in returns remained roughly the same. As for education, returns to education for both genders increased between 1980 and 2010; at the same time, differences between men and women in returns to education expanded (see Fig.  2 b). These trends could lead us to expect that the portion of the wage gap explained by differences in returns to education would increase over time, but the findings show otherwise. This is possibly because the advantage gained by women in terms of acquiring higher education was enough to offset the effect of their growing disadvantage in terms of returns to education.

To provide further validation to the conclusion above we apply the same decomposition after disaggregating the analysis by two broad categories of occupations and industries. Specifically, we distinguish between manual and semiprofessional occupations vs. professional and managerial occupations, and between the manufacturing industry versus the service industry. The results (presented in the appendix) show that in all four segments most of the gender wage gap is related to gender differences in returns (i.e., the wage structure/coefficients), and only a small fraction is related to gender differences in skills between men and women (endowments). That said, there is a clear variation between segments in the extent to which gender differences in returns to skills explain gender differences in pay. Understanding this variation, though, would require delving into the unique characteristics of each segment, which is beyond the scope of this paper. We therefore refer here to the most significant segment with regard to overtime changes in gender gaps in recent decades—the managerial and professional occupations.

Managerial and professional occupations not only have the highest levels of education and skills and offer greater rewards for skills, but also found to be specifically important in relation to women’s upward mobility in the labor market, and the gender pay gaps in particular (Hegewisch & Hartmann, 2014 ). The rise of women’s education and skills in the past decades has motivated women’s entry into highly paid male-dominated occupations. This has led to one of the most important changes in gender inequality in the labor market: the decline of gender occupational segregation (England, 2010 ). Of a particular importance is the inflow of women into high paying managerial and professional occupations (Mandel, 2012 ; Roos & Stevens, 2018 ). Between 1980 and 2010 women increased their share in these occupations from 26% to 43% (authors’ calculations from the Current Population Survey data (US-CPS)). While in 1980 only 9% of women worked in these occupations, in 2010 this figure nearly doubled, reaching 17.1% (as compared to 21.5% of men). This trend may seem as a promising path to greater gender equality, but the reality is more complicated. Wages in these occupations are higher, but so is wage inequality (Gottlieb et al., 2019 ), which reflects on gender inequality. While the overall gender pay gap has declined between 1980 and 2010, it actually has not changed within the managerial and professional occupations, as the women to men wage gap remained at about 69%.

Table 3 and Fig.  5 , which present the results of the decomposition analysis after limiting the sample to workers in managerial and professional occupations, reveal similar but more pronounced results as those found in the entire labor market. Specifically, Fig.  5 shows that, as found earlier, the portion of the gap explained by gender differences in skills is small and in decline (falling from 19.1% in 1980 to 9.6% in 2010). In contrast, the portion of the gap explained by gender differences in returns to skills is much larger. Furthermore, between 1980 and 2010 the portion of the gap explained by gender differences in both experience and education substantially increased. The portion related to returns to education as well as the portion related to returns to experience are much larger in these occupations. These results indicate that in the more lucrative jobs the unequal returns to skills serve as an even greater obstacle for achieving gender equality. Given women’s rapid integration in these occupations, and the opportunity they open for women’s economic advancement, the restrictions that the wage structure imposes on women’s relative wage become more evident.

Blinder-Oaxaca threefold decomposition of the gender wage gap among employees in managerial and professional occupations (selected findings)

It is important to highlight that the current analysis, which follows Blau and Kahn’s ( 2017a ) work, focuses on workers that demonstrate relatively high commitment to the labor market (working full-time and for at least 26 weeks in the year before they were surveyed). Within this group, we found that human capital differences have little effect on the gender wage gap. But they may still have a larger effect on the gender gap outside the primary labor market, where there are more women than men, and where levels of human capital, as well as wages, are lower. Thus, our analysis does not capture the total gender wage gap in the labor market. In fact, our evaluations of the wage gap are likely to be underestimated, given that the women in our sample are more selective than the men. Our findings thus show that it is within the primary labor market—exactly where human capital is rewarded the most and where high-skilled women can expect to realize their earnings potential—that women find themselves subject to a different set of rules compared to men. This is what we term as a gender-specific wage structure, and it begs the obvious question: why is it so? We propose a few possible answers to this question in the concluding section of this paper.

6 Discussion

This paper seeks to fill a theoretical and empirical lacuna in the current literature on gender wage inequality in the labor market. Theoretically, the paper challenges predominant conceptualization of the wage structure as gender-neutral, rather emphasizing the contribution that it makes to the gender wage gap. Considering previous empirical evidence, most studies that decomposed the gender wage gap dedicated much of their attention to that part of the gap explained by gender differences in productivity-enhancing characteristics (i.e., the ‘explained’ portion). The ‘unexplained’ portion of the gap, which refers to the residual that cannot be explained by differences in characteristics, stems from the wage structure, i.e., the market returns to human capital. These returns have been assumed to be gender-neutral, and their relation to gender mediated by differences in human capital between the gender groups. Gender differences in these returns were attributed to market failure (discrimination) or measurement error, and thus received little scholarly attention in decomposition research. Studies that did document gender differences in returns to human capital usually did not examine the contribution these differences made to the gender wage gap. Furthermore, these studies focused on the rates of return to investment, i.e., gender differences in the relative (gender specific) value of human capital, neglecting the differences between men and women in the absolute monetary value of human capital—the measure that matters when addressing gender inequality.

Our findings show that the key to understand the gender wage gap and its persistence lies not in the different characteristics of male and female workers, but mainly in the fact that women are rewarded less than men for their skills. Gender differences in absolute returns to human capital are prevalent and account for much of the wage gap, and their effect increases over time. This conclusion holds even when disaggregating the labor market to broad categories of occupations and industries. Further research is needed to identify the sources of the gender differences in returns; the first step, made by the current paper, is to acknowledge their importance to the remaining gender pay gap.

In search for explanations for differential returns to human capital, we need to distinguish between two types of explanations: those that rely on differences in the behaviors of men and women in the labor market, and those that focus on the structure of the labor market. The first type relates to identifying the mechanisms and processes that explain how and why women, as individuals, end up in positions with lower rewards. Socialization and social control explain women’s compliance with norms that channel them into positions and employment patterns that pay less. This includes, first and foremost, the consequences of women’s commitment to unpaid domestic and care work, especially childrearing, that hinders their potential to obtain pay raises and promotions—or, in other words, to maximize the rewards for their skills (Budig & England, 2001 ; England et al., 2016 ). One example is the fact that women’s availability for working extra hours is constrained by their commitment to domestic and care work, and so they are less likely to exploit the wage premium enjoyed by workers who can work long hours and overtime (Cha & Weeden, 2014 ). Gender norms and expectations at early life stages also account for segregation between qualitative categories within quantitative educational levels. The outcome is that women are directed into fields that pay less due to factors such as reduced workload or low productivity. These mechanisms, which operate at the individual level, not only account for the different human capital acquired by men and women (the explained portion) but also for the lower rewards that women get for the “same” human capital (unexplained), or the gender pay gap within the same occupation.

The second type of explanations for differential returns to human capital consists of explanations that focus on the structural mechanisms and processes that shape the different opportunity structures that men and women face in the labor market. At the macro level, deeply rooted gender beliefs lead to the low evaluation of fields, jobs and tasks predominantly undertaken by women or signified as feminine. In this case, the lower rewards to a job or occupation apply to all workers, men as well as women. Nevertheless, since women by definition tend to work in female-dominated jobs and occupations, the outcome is that many women receive low rewards for their skills because they acquired female-typical skills that are undervalued by the labor market. Furthermore, as more women enter fields and occupations that were traditionally dominated by men, these fields and occupations suffer from devaluation and the rewards they offer decline (England, 1992 ; Levanon et al., 2009 ; Mandel, 2018 ). Note that gender inequality also exists within occupations, partly due to the lower evaluation of specific tasks—predominantly those carried out by women (Bizopoulou, 2019 ; Goldin, 2014 ). Supporting this claim, when focusing on the managerial and professional occupations, high paying occupations to which women have entered in masses in the past few decades, we indeed find that in these occupations a greater portion of the gap is related to differences in the returns to skills between men and women (especially to education), and that this portion increases even further over time.

On a final note, the decomposition of the gender wage gap into ‘explained’ and ‘unexplained’ portions, and its identification with ‘individual’ and ‘structural’ mechanisms of gender inequality is not merely analytical but has particular importance for policymaking. It points at the elements that have the largest impact on the gender wage gap, and therefore can guide the design of interventions that would be most effective and meaningful. Normative changes that encourage women (and girls) to invest in their human capital have certainly achieved a lot; women have made substantial advancements in the acquisition of labor market skills. However, the results presented in this paper indicate that it is vital to focus more on the institutional and structural factors that prevent women from fully utilizing their skills. Policymakers who wish to target the gender wage gap should therefore be aware of the economic consequences of socialization and gender beliefs that account for the low evaluation of women, as well as their education, skills, and occupations.

Blau and Kahn also present a more inclusive specification, that controls for categories of occupations and industries (fourteen industry and twenty occupation dummy variables are included in the model). However, interpreting the returns to skill while controlling for segments that has almost no variation in education is questionable. We avoided including the occupations and industry variables, not only because their interpretation is ambiguous (as Blau and Kahn correctly acknowledge (p. 797)), but also because they are inherently related to education and experience. Thus, controlling for them would mask the gender differences in these returns (Reid and Rubin 2003 ). We do, however, disaggregate the results by broad categories of occupations and industries. The findings are presented in the appendix and discussed in the findings section.

Instead of the common, and problematic, measure that approximates work experience by the subtraction of the number of years of schooling plus six from the respondent’s age (Blau and Kahn 2013 ; Olsen and Walby 2004 ).

See the appendix in Blau and Kahn’s ( 2017a ) paper for more information about the construction of the variables.

More specifically, the part of the wage gap explained by education differences was 2.7% in 1980 and -7.9% in 2010 (suggesting that equalization of education in that year would increase the wage gap, as women attained more education than men despite earning less); the part explained by differences in work experience was 23.9% and 15.9% in 1980 and 2010 respectively.

Note that in a model that includes years of schooling as a single measure of education, we do find that in 1980 (but not in 2010) there is a statistically significant interaction with gender, consistent with previous studies that found that women’s rates of return to schooling are higher than men’s during the 1980s and 1990s (Dougherty 2005 ; Hubbard 2011 ).

When calculating the net effect of postgraduate education (controlling for years of schooling), we find that in 1980, a postgraduate degree led to a 23% wage premium on average for women, as compared to 6% for men.

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Appendix: Decomposition of the gender wage gap – disaggregation by categories of occupations and industries

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Rotman, A., Mandel, H. Gender-Specific Wage Structure and the Gender Wage Gap in the U.S. Labor Market. Soc Indic Res 165 , 585–606 (2023). https://doi.org/10.1007/s11205-022-03030-4

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The Enduring Grip of the Gender Pay Gap

Table of contents.

The gender pay gap – the difference between the earnings of men and women – has barely closed in the United States in the past two decades. In 2022, American women typically earned 82 cents for every dollar earned by men. That was about the same as in 2002, when they earned 80 cents to the dollar. The slow pace at which the gender pay gap has narrowed this century contrasts sharply with the progress in the preceding two decades: In 1982, women earned just 65 cents to each dollar earned by men.

There is no single explanation for why progress toward narrowing the pay gap has all but stalled in the 21st century. Women generally begin their careers closer to wage parity with men, but they lose ground as they age and progress through their work lives, a pattern that has remained consistent over time. The pay gap persists even though women today are more likely than men to have graduated from college. In fact, the pay gap between college-educated women and men is not any narrower than the one between women and men who do not have a college degree. This points to the dominant role of other factors that still set women back or give men an advantage.

One of these factors is parenthood. Mothers ages 25 to 44 are less likely to be in the labor force than women of the same age who do not have children at home, and they tend to work fewer hours each week when employed. This can reduce the earnings of some mothers, although evidence suggests the effect is either modest overall or short-lived for many. On the other hand, fathers are more likely to be in the labor force – and to work more hours each week – than men without children at home. This is linked to an increase in the pay of fathers – a phenomenon referred to as the “ fatherhood wage premium ” – and tends to widen the gender pay gap.

Related: Gender pay gap in U.S. hasn’t changed much in two decades

Family needs can also influence the types of jobs women and men pursue , contributing to gender segregation across occupations. Differential treatment of women, including gender stereotypes and discrimination , may also play a role. And the gender wage gap varies widely by race and ethnicity.

Pew Research Center conducted this study to better understand how women’s pay compared with men’s pay in the U.S. in the economic aftermath of the COVID-19 outbreak .

The study is based on the analysis of monthly Current Population Survey (CPS) data from January 1982 to December 2022 monthly files ( IPUMS ). The CPS is the U.S. government’s official source for monthly estimates of unemployment . For a quarter of the sample each month, the CPS also records data on usual hourly earnings for hourly workers and usual weekly earnings and hours worked for other workers. In this report, monthly CPS files were combined to create annual files to boost sample sizes and to analyze the gender pay gap in greater detail.

The comparison between women’s and men’s pay is based on their median hourly earnings. For workers who are not hourly workers, hourly earnings were computed as the ratio of usual weekly earnings to usual weekly hours worked. The samples include employed workers ages 16 and older with positive earnings, working full time or part time, including those for whom earnings were imputed by the Census Bureau . Self-employed workers are excluded because their earnings are not recorded in the CPS.

The COVID-19 outbreak affected data collection efforts by the U.S. government in its surveys, especially in 2020 and 2021, limiting in-person data collection and affecting the response rate. It is possible that some measures of economic outcomes and how they vary across demographic groups are affected by these changes in data collection.

“Mothers” and “fathers” refer to women and men 16 and older who have an own child younger than 18 living in the household.

The U.S. labor force, used interchangeably with the workforce in this analysis, consists of people 16 and older who are either employed or actively looking for work.

White, Black and Asian workers include those who report being only one race and who are not Hispanic. Hispanics are of any race. Asian workers include Pacific Islanders. Other racial and ethnic groups are included in all totals but are not shown separately.

“High school graduate” refers to those who have a high school diploma or its equivalent, such as a General Education Development (GED) certificate, and those who had completed 12th grade, but their diploma status was unclear (those who had finished 12th grade but not received a diploma are excluded). “Some college” include workers with an associate degree and those who attended college but did not obtain a degree.

How the gender pay gap increases with age

Younger women – those ages 25 to 34 and early in their work lives – have edged closer to wage parity with men in recent years. Starting in 2007, their earnings have consistently stood at about 90 cents to the dollar or more compared with men of the same age. But even as pay parity might appear in reach for women at the start of their careers, the wage gap tends to increase as they age.

Consider, for example, women who were ages 25 to 34 in 2010. In that year, they earned 92% as much as men their age, compared with 83% for women overall. But by 2022, this group of women, now ages 37 to 46, earned only 84% as much as men of the same age. This pattern repeats itself for groups of women who were ages 25 to 34 in earlier years – say, 2005 or 2000 – and it may well be the future for women entering the workforce now.

A good share of the increase in the gender pay gap takes place when women are between the ages of 35 and 44. In 2022, women ages 25 to 34 earned about 92% as much as men of the same ages, but women ages 35 to 44 and 45 to 54 earned 83% as much. The ratio dropped to 79% among those ages 55 to 64. This general pattern has not changed in at least four decades.

The increase in the pay gap coincides with the age at which women are more likely to have children under 18 at home. In 2022, 40% of employed women ages 25 to 34 had at least one child at home. The same was true for 66% of women ages 35 to 44 but for fewer – 39% – among women ages 45 to 54. Only 6% of employed women ages 55 to 64 had children at home in 2022.

Similarly, the share of employed men with children at home peaks between the ages of 35 to 44, standing at 58% in 2022. This is also when fathers tend to receive higher pay, even as the pay of employed mothers in same age group is unaffected.

Mothers with children at home tend to be less engaged with the workplace, while fathers are more active

Parenthood leads some women to put their careers on hold, whether by choice or necessity, but it has the opposite effect among men. In 2022, 70% of mothers ages 25 to 34 had a job or were looking for one, compared with 84% of women of the same age without children at home. This amounted to the withdrawal of 1.4 million younger mothers from the workforce. Moreover, when they are employed, younger mothers tend to put in a shorter workweek – by two hours per week, on average – than other women their age. Reduced engagement with the workplace among younger mothers is also a long-running phenomenon.

Fathers, however, are more likely to hold a job or be looking for one than men who don’t have children at home, and this is true throughout the prime of their working years , from ages 25 to 54. Among those who do have a job, fathers also work a bit more each week, on average, than men who do not have children at home.

As a result, the gender gap in workplace activity is greater among those who have children at home than among those who do not. For example, among those ages 35 to 44, 94% of fathers are active in the workforce, compared with 75% of mothers – a gap of 19 percentage points. But among those with no children at home in this age group, 84% of men and 78% of women are active in the workforce – a gap of 6 points.

These patterns contribute to the gap in workplace activity between men and women overall. As of 2022, 68% of men ages 16 and older – with or without children at home – are either employed or seeking employment. That compares with 57% of women, a difference of 11 percentage points. This gap was as wide as 24 points in 1982, but it narrowed to 14 points by 2002. Men overall also worked about three hours more per week at a job than women in 2022, on average, down from a gap of about six hours per week in 1982.

Employed mothers earn about the same as similarly educated women without children at home; both groups earn less than fathers

Parenthood affects the hourly earnings of employed women and men in unexpected ways. While employed mothers overall appear to earn less than employed women without children at home, the gap is driven mainly by differences in educational attainment between the two groups. Among women with similar levels of education, there is little gap in the earnings of mothers and non-mothers. However, fathers earn more than other workers, including other men without children at home, regardless of education level. This phenomenon – known as the fatherhood wage premium – is one of the main ways that parenthood affects the gender pay gap among employed workers.

Among employed men and women, the impact of parenting is felt most among those ages 25 to 54, when they are most likely to have children under 18 at home. In 2022, mothers ages 25 to 34 earned 85% as much as fathers that age, but women without children at home earned 97% as much as fathers. In contrast, employed women ages 35 to 44 – with or without children – both earned about 80% as much as fathers. The table turns for women ages 45 to 54, with mothers earning more than women with no children at home. Among those ages 35 to 44 or 45 to 54, men without children earned only 84% as much as fathers.

When the earnings of mothers are compared with those of women without children at home who have the same level of education, the differences either narrow or go away. Among employed women ages 25 to 34 with at least a bachelor’s degree, both mothers and women without children at home earned 80% as much as fathers in 2022. Among women ages 25 to 34 with a high school diploma and no further education, mothers earned 79% as much as fathers and women with no children at home earned 84% as much. The narrowing of the gap in earnings of mothers and women without children at home after controlling for education level also extends to other age groups.

Thus, among the employed, the effect of parenthood on the gender pay gap does not seem to be driven by a decrease in mothers’ earnings relative to women without children at home. Instead, the widening of the pay gap with parenthood appears to be driven more by an increase in the earnings of fathers. Fathers ages 25 to 54 not only earn more than mothers the same age, they also earn more than men with no children at home. Nonetheless, men without children at home still earn more than women with or without children at home.

Although there is little gap in the earnings of employed mothers and women with no children at home who have the same level of education, there is a lingering gap in workplace engagement between the two groups. Whether they had at least a bachelor’s degree or were high school graduates, mothers ages 25 to 34 are less likely to hold a job or be looking for one. Similarly, younger mothers on average work fewer hours than women without children at home each week, regardless of their education level. The opposite is true for fathers compared with men without children at home.

Progress in closing the gender pay gap has slowed despite gains in women’s education

The share of women with at least a bachelor’s degree has increased steadily since 1982 – and faster than among men. In 1982, 20% of employed women ages 25 and older had a bachelor’s degree or higher level of education, compared with 26% of employed men. By 2022, 48% of employed women had at least a bachelor’s degree, compared with 41% of men. Still, women did not see the pay gap close to the same extent from 2002 to 2022 as they did from 1982 to 2002.

In part, this may be linked to how the gains from going to college have changed in recent decades, for women and men alike. The college wage premium – the boost in earnings workers get from a college degree – increased rapidly during the 1980s. But the rise in the premium slowed down over time and came to a halt around 2010. This likely reduced the relative growth in the earnings of women.

Although gains in education have raised the average earnings of women and have narrowed the gender pay gap overall, college-educated women are no closer to wage parity with their male counterparts than other women. In 2022, women with at least a bachelor’s degree earned 79% as much as men who were college graduates, and women who were high school graduates earned 81% as much as men with the same level of education. This underscores the challenges faced by women of all education levels in closing the pay gap.

Notably, the gender wage gap has closed more among workers without a four-year college degree than among those who do have a bachelor’s degree or more education. For example, the wage gap for women without a high school diploma narrowed from 62% in 1982 to 83% in 2022 relative to men at the same education level. But it closed only from 69% to 79% among bachelor’s degree holders over the same period. This is because only men with at least a bachelor’s degree experienced positive wage growth from 1982 to 2022; all other men saw their real wages decrease. Meanwhile, the real earnings of women increased regardless of their level of education.

As women have improved their level of education in recent decades, they’ve also increased their share of employment in higher-paying occupations, such as managerial, business and finance, legal, and computer, science and engineering (STEM) occupations. In 1982, women accounted for only 26% of employment in managerial occupations. By 2022, their share had risen to 40%. Women also substantially increased their presence in social, arts and media occupations. Over the same period, the shares of women in several lower-paying fields, such as administrative support jobs and food preparation and serving occupations, fell significantly.

Even so, women are still underrepresented in managerial and STEM occupations – along with construction, repair and production, and transportation occupations – when compared with their share of employment overall. And there has been virtually no change in the degree to which women are over represented in education, health care, and personal care and services occupations – the last of which are lower paying than the average across all occupations. The distribution of women and men across occupations remains one of the drivers of the gender pay gap . But the degree to which this distribution is the result of personal choices or gender stereotypes is not entirely clear.

Gender pay gap differs widely by race and ethnicity

Looking across racial and ethnic groups, a wide gulf separates the earnings of Black and Hispanic women from the earnings of White men. 3 In 2022, Black women earned 70% as much as White men and Hispanic women earned only 65% as much. The ratio for White women stood at 83%, about the same as the earnings gap overall, while Asian women were closer to parity with White men, making 93% as much.

The pay gap narrowed for all groups of women from 1982 to 2022, but more so for White women than for Black and Hispanic women. The earnings gap for Asian women narrowed by about 17 percentage points from 2002 to 2022, but data for this group is not available for 1982.

To some extent, the gender wage gap varies by race and ethnicity because of differences in education, experience, occupation and other factors that drive the gender wage gap for women overall. But researchers have uncovered new evidence of hiring discrimination against various racial and ethnic groups, along with discrimination against other groups, such as LGBTQ and disabled workers. Discrimination in hiring may feed into differences in earnings by shutting out workers from opportunities.

Broader economic forces may impact men’s and women’s earnings in different ways

Changes in the gender pay gap are also shaped by economic factors that sometimes drive men’s and women’s earnings in distinctive ways. Because men and women tend to work in different types of jobs and industries, their earnings may respond differently to external pressures.

More specifically, men’s earnings essentially didn’t change from 1982 to 2002. Potential reasons for that include a more rapid decline in union membership among men, a shift away from jobs calling for more physical skills, and global competition that sharply reduced employment in manufacturing in the 1980s. At the same time, women’s earnings increased substantially as they raised their level of education and shifted toward higher-paying occupations.

But in some ways, the economic climate has proved less favorable for women this century. For reasons that are not entirely clear, women’s employment was slower to recover from the Great Recession of 2007-2009. More recently, the COVID-19 recession took on the moniker “ she-cession ” because of the pressure on jobs disproportionately held by women . Amid a broader slowdown in earnings growth from 2000 to 2015, the increase in women’s earnings from 2002 to 2022 was not much greater than the increase in men’s earnings, limiting the closure in the gender pay gap over the period.

What’s next for the gender pay gap?

Higher education, a shift to higher-paying occupations and more labor market experience have helped women narrow the gender pay gap since 1982. But even as women have continued to outpace men in educational attainment, the pay gap has been stuck in a holding pattern since 2002, ranging from 80 to 85 cents to the dollar.

More sustained progress in closing the pay gap may depend on deeper changes in societal and cultural norms and in workplace flexibility that affect how men and women balance their careers and family lives . Even in countries that have taken the lead in implementing family-friendly policies, such as Denmark, parenthood continues to drive a significant wedge in the earnings of men and women. New research suggests that family-friendly policies in the U.S. may be keeping the pay gap from closing. Gender stereotypes and discrimination, though difficult to quantify, also appear to be among the “last-mile” hurdles impeding further progress.

What is the gender wage gap in your metropolitan area? Find out with our pay gap calculator

• It is also worth noting that even if the hourly earnings of mothers are not affected, their weekly or annual pay is reduced in line with the reduction in the hours worked. ↩
• In part, this is because the age of women at first birth varies by educational attainment . Motherhood among women with a bachelor’s degree or higher level of education occurs at an older age than among women without a bachelor’s degree. ↩
• Although Asian men earned about 24% more than White men at the median in 2022, comparisons in this section are drawn with reference to White men. In 2022, White men accounted for about one-third of total employment in the U.S., compared with about 3% for Asian men. ↩

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Gender wage gap in healthcare has narrowed, but remains

Media Contact: Susan Gregg - 206-390-3226, [email protected]

A wage gap still exists between men and women in the healthcare workforce (as well as the workforce at large).  According to Economic Policy Institute , the U.S. wage gap in general was 21.8 % in 2023, little changed over decades. Worldwide, women face a 24% gap across the healthcare sector, according to the World Health Organization . 

So far, most of this research has focused on the higher levels of the medical fields — positions requiring an M.D. or Ph.D., or nursing degree.

Yet little research has examined the wage gap between men and women in healthcare fields that don’t require an M.D. or Ph.D., noted  Bianca Frogner , director of the Center for Health Workforce Studies at the University of Washington School of Medicine.

Or how to eliminate these gaps.

In a  paper published in Health Affairs Scholar, Frogner and colleagues found that the wage gap persists and that little is being done to solve it, she said. 

The researchers examined wage data from the Annual Social and Economic Supplement of the Current Population Survey, gathered between 2003 and 2021, to see how the gender wage gap in various healthcare fields has changed over the last two decades. They reported some good news in terms of representation by gender.

Women increased 8% in healthcare positions that require a master’s degree, and 42% in positions at the doctorate/professional level. At the bachelor’s degree level, however, growth was stagnant, with no change since 2003. Overall, women make up 50% of employees for those jobs that require a doctorate degree, up from 35% two decades ago, the report noted.

While those increases in representation are encouraging, the wage gap numbers are not, Frogner said.  The adjusted wage gap between women and men is 61%, the largest among workers in high-education healthcare fields such as physicians and other advanced practitioners. 

In 2021, the gender wage gap was the lowest among workers with a bachelor’s degree (88%), followed by those with an associate degree (82%), some college (77%), master’s degree (77%), high school degree (72%), less than high school (71%), and professional school/doctorate degree (61%).

The gender wage gap has stagnated or grown larger in some lower-education occupations in which men’s percentage of the workforce has increased. In these areas, Frogner noted, men are often promoted to management positions over women who compose more of the workforce.

For people of color who work in the medical healthcare field, the gap is worse, the report noted.

Hispanic women earned just 57 cents on the dollar and Black women earned 64 cents for every dollar earned by white non-Hispanic men. 

Training women for more leadership positions, even if they are in entry-level jobs, is one solution to the problem, Frogner said.

“We need to help women in low wage jobs find more opportunity for leadership, if not through education, then expanding their leadership roles” she said. 

For women doctors, the wage gap might start with hiring, but it is worsened when women spend more time with their patients than male doctors do, and by the average pay they receive, she noted. 

Moreover, Frogner said, the act of hiring women for these jobs is not enough. Factors to retain them must be considered — not only equal pay but also healthcare benefits, childcare, transportation options and flexible schedules.

“We need to make healthcare jobs more attractive, especially for women who may have been sidelined during the pandemic,” she said. “It doesn’t hurt to have more women as leaders in healthcare. We certainly don’t have enough.”

“We know that more women than men left healthcare during the pandemic,” she said. “We want to find out who is coming back.”

The University of Minnesota School of Public Health collaborated with Frogner’s team on this study. 

For details about UW Medicine, please visit  http://uwmedicine.org/about .